{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5 再调n_estimators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "import xgboost as xgb\n",
    "\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "from sklearn.metrics import log_loss\n",
    "\n",
    "from matplotlib import pyplot\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# read in data，数据在xgboost安装的路径下的demo目录,现在我们将其copy到当前代码下的data目录\n",
    "dpath = './data/'\n",
    "dtrain = xgb.DMatrix(dpath + 'RentListingInquries_FE_train.bin')\n",
    "dtest = xgb.DMatrix(dpath + 'RentListingInquries_FE_test.bin')\n",
    "train = pd.read_csv(dpath + 'RentListingInquries_FE_train.csv')\n",
    "test = pd.read_csv(dpath + 'RentListingInquries_FE_test.csv')\n",
    "\n",
    "\n",
    "\n",
    "# drop ids and get labels\n",
    "y_train = train['interest_level']\n",
    "train = train.drop([\"interest_level\"], axis=1)\n",
    "X_train = np.array(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "各类样本不均衡，交叉验证是采用StratifiedKFold，在每折采样时各类样本按比例采样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 准备交叉验证\n",
    "kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "再次调整弱分类器数目"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def modelfit(alg, X_train, y_train, useTrainCV=True, cv_folds=None, early_stopping_rounds=100):\n",
    "    \n",
    "    if useTrainCV:\n",
    "        xgb_param = alg.get_xgb_params()\n",
    "        xgb_param['num_class'] = 3\n",
    "        \n",
    "        xgtrain = xgb.DMatrix(X_train, label = y_train)\n",
    "        \n",
    "        cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], folds =cv_folds,\n",
    "                         metrics='mlogloss', early_stopping_rounds=early_stopping_rounds)\n",
    "        \n",
    "        n_estimators = cvresult.shape[0]\n",
    "        alg.set_params(n_estimators = n_estimators)\n",
    "        \n",
    "        print(cvresult)\n",
    "        #result = pd.DataFrame(cvresult)   #cv缺省返回结果为DataFrame\n",
    "        #result.to_csv('my_preds.csv', index_label = 'n_estimators')\n",
    "        cvresult.to_csv('my_preds4_2_3_699.csv', index_label = 'n_estimators')\n",
    "        \n",
    "        # plot\n",
    "        test_means = cvresult['test-mlogloss-mean']\n",
    "        test_stds = cvresult['test-mlogloss-std'] \n",
    "        \n",
    "        train_means = cvresult['train-mlogloss-mean']\n",
    "        train_stds = cvresult['train-mlogloss-std'] \n",
    "\n",
    "        x_axis = range(0, n_estimators)\n",
    "        pyplot.errorbar(x_axis, test_means, yerr=test_stds ,label='Test')\n",
    "        pyplot.errorbar(x_axis, train_means, yerr=train_stds ,label='Train')\n",
    "        pyplot.title(\"XGBoost n_estimators vs Log Loss\")\n",
    "        pyplot.xlabel( 'n_estimators' )\n",
    "        pyplot.ylabel( 'Log Loss' )\n",
    "        pyplot.savefig( 'n_estimators4_2_3_699.png' )\n",
    "    \n",
    "    #Fit the algorithm on the data\n",
    "    alg.fit(X_train, y_train, eval_metric='mlogloss')\n",
    "        \n",
    "    #Predict training set:\n",
    "    train_predprob = alg.predict_proba(X_train)\n",
    "    logloss = log_loss(y_train, train_predprob)\n",
    "\n",
    "        \n",
    "    #Print model report:\n",
    "    print(\"logloss of train :\" )\n",
    "    print(logloss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     test-mlogloss-mean  test-mlogloss-std  train-mlogloss-mean  \\\n",
      "0              1.039083           0.000313             1.038228   \n",
      "1              0.988843           0.000568             0.987140   \n",
      "2              0.945620           0.000612             0.942944   \n",
      "3              0.908462           0.000623             0.904889   \n",
      "4              0.876630           0.000407             0.872220   \n",
      "5              0.848242           0.000594             0.842883   \n",
      "6              0.824115           0.000682             0.817963   \n",
      "7              0.802872           0.000884             0.795920   \n",
      "8              0.784181           0.001111             0.776535   \n",
      "9              0.767760           0.001452             0.759342   \n",
      "10             0.752945           0.001712             0.743681   \n",
      "11             0.739750           0.001451             0.729733   \n",
      "12             0.728428           0.001330             0.717563   \n",
      "13             0.718154           0.001014             0.706498   \n",
      "14             0.709015           0.001225             0.696595   \n",
      "15             0.700770           0.001467             0.687405   \n",
      "16             0.693272           0.001587             0.679048   \n",
      "17             0.686716           0.001792             0.671695   \n",
      "18             0.680834           0.001737             0.665070   \n",
      "19             0.675369           0.001495             0.658934   \n",
      "20             0.670467           0.001849             0.653355   \n",
      "21             0.666082           0.001524             0.648235   \n",
      "22             0.662046           0.001487             0.643334   \n",
      "23             0.658350           0.001441             0.638874   \n",
      "24             0.654788           0.001458             0.634599   \n",
      "25             0.651577           0.001747             0.630593   \n",
      "26             0.648491           0.001659             0.626911   \n",
      "27             0.645769           0.001596             0.623638   \n",
      "28             0.643314           0.001725             0.620413   \n",
      "29             0.640957           0.001907             0.617355   \n",
      "..                  ...                ...                  ...   \n",
      "204            0.592192           0.002110             0.477793   \n",
      "205            0.592048           0.002168             0.477312   \n",
      "206            0.592053           0.002267             0.476879   \n",
      "207            0.592074           0.002332             0.476413   \n",
      "208            0.592055           0.002374             0.475979   \n",
      "209            0.592120           0.002341             0.475519   \n",
      "210            0.592117           0.002435             0.475057   \n",
      "211            0.592095           0.002389             0.474712   \n",
      "212            0.592109           0.002413             0.474340   \n",
      "213            0.592134           0.002297             0.473875   \n",
      "214            0.592149           0.002246             0.473468   \n",
      "215            0.592170           0.002208             0.473040   \n",
      "216            0.592051           0.002224             0.472589   \n",
      "217            0.592142           0.002113             0.472141   \n",
      "218            0.592197           0.002098             0.471728   \n",
      "219            0.592138           0.002148             0.471237   \n",
      "220            0.592119           0.002217             0.470745   \n",
      "221            0.592023           0.002244             0.470279   \n",
      "222            0.591978           0.002266             0.469903   \n",
      "223            0.592017           0.002298             0.469395   \n",
      "224            0.591999           0.002308             0.468941   \n",
      "225            0.591993           0.002310             0.468570   \n",
      "226            0.591996           0.002241             0.468274   \n",
      "227            0.592015           0.002223             0.467927   \n",
      "228            0.592068           0.002128             0.467448   \n",
      "229            0.592106           0.002029             0.466977   \n",
      "230            0.592073           0.002046             0.466595   \n",
      "231            0.592060           0.002074             0.466243   \n",
      "232            0.591995           0.002056             0.465810   \n",
      "233            0.591977           0.002010             0.465395   \n",
      "\n",
      "     train-mlogloss-std  \n",
      "0              0.000433  \n",
      "1              0.000636  \n",
      "2              0.000591  \n",
      "3              0.000714  \n",
      "4              0.000282  \n",
      "5              0.000059  \n",
      "6              0.000272  \n",
      "7              0.000469  \n",
      "8              0.000312  \n",
      "9              0.000337  \n",
      "10             0.000369  \n",
      "11             0.000227  \n",
      "12             0.000295  \n",
      "13             0.000573  \n",
      "14             0.000407  \n",
      "15             0.000319  \n",
      "16             0.000169  \n",
      "17             0.000195  \n",
      "18             0.000346  \n",
      "19             0.000485  \n",
      "20             0.000084  \n",
      "21             0.000503  \n",
      "22             0.000544  \n",
      "23             0.000752  \n",
      "24             0.001035  \n",
      "25             0.000932  \n",
      "26             0.001053  \n",
      "27             0.001180  \n",
      "28             0.001199  \n",
      "29             0.001035  \n",
      "..                  ...  \n",
      "204            0.001740  \n",
      "205            0.001738  \n",
      "206            0.001741  \n",
      "207            0.001715  \n",
      "208            0.001766  \n",
      "209            0.001770  \n",
      "210            0.001762  \n",
      "211            0.001781  \n",
      "212            0.001659  \n",
      "213            0.001771  \n",
      "214            0.001832  \n",
      "215            0.001871  \n",
      "216            0.002004  \n",
      "217            0.001913  \n",
      "218            0.001965  \n",
      "219            0.001957  \n",
      "220            0.001951  \n",
      "221            0.001940  \n",
      "222            0.001900  \n",
      "223            0.001927  \n",
      "224            0.001868  \n",
      "225            0.001893  \n",
      "226            0.001877  \n",
      "227            0.001862  \n",
      "228            0.001853  \n",
      "229            0.001775  \n",
      "230            0.001669  \n",
      "231            0.001699  \n",
      "232            0.001639  \n",
      "233            0.001619  \n",
      "\n",
      "[234 rows x 4 columns]\n",
      "logloss of train :\n",
      "0.490557654176\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAETCAYAAAA/NdFSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njPGp/w+QeLjqtQ45TgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd189e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#调整max_depth和min_child_weight之后再次调整n_estimators(6,4)\n",
    "xgb5 = XGBClassifier(\n",
    "        learning_rate =0.1,\n",
    "        n_estimators=500,  #数值大没关系，cv会自动返回合适的n_estimators\n",
    "        max_depth=6,\n",
    "        min_child_weight=4,\n",
    "        gamma=0,\n",
    "        subsample=0.3,\n",
    "        colsample_bytree=0.8,\n",
    "        colsample_bylevel=0.7,\n",
    "        objective= 'multi:softprob',\n",
    "        seed=3)\n",
    "\n",
    "modelfit(xgb5, X_train, y_train, cv_folds = kfold)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1MAAANGCAYAAAAYo9S0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngylJkiRJKsBgSpIkSZIK+P/yB7EnsxxOOgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc296f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cvresult = pd.DataFrame.from_csv('my_preds4_2_3_699.csv')\n",
    "\n",
    "cvresult = cvresult.iloc[40:]\n",
    "# plot\n",
    "test_means = cvresult['test-mlogloss-mean']\n",
    "test_stds = cvresult['test-mlogloss-std'] \n",
    "        \n",
    "train_means = cvresult['train-mlogloss-mean']\n",
    "train_stds = cvresult['train-mlogloss-std'] \n",
    "\n",
    "x_axis = range(40,cvresult.shape[0]+40)\n",
    "        \n",
    "fig = pyplot.figure(figsize=(10, 10), dpi=100)\n",
    "pyplot.errorbar(x_axis, test_means, yerr=test_stds ,label='Test')\n",
    "pyplot.errorbar(x_axis, train_means, yerr=train_stds ,label='Train')\n",
    "pyplot.title(\"XGBoost n_estimators vs Log Loss\")\n",
    "pyplot.xlabel( 'n_estimators' )\n",
    "pyplot.ylabel( 'Log Loss' )\n",
    "pyplot.savefig( 'n_estimators_detail4_2_3_699.png' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
